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英文文献：Estimating Stochastic Volatility Models using Prediction-based Estimating Functions-使用基于预测的估计函数估计随机波动模型
英文文献作者：Asger Lunde,Anne Floor Brix
英文文献摘要：
In this paper prediction-based estimating functions (PBEFs), introduced in S?rensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF based estimator in practice.
摘要prediction-based估计函数(PBEFs),介绍了S?rensen(2000),进行了综述和PBEFs赫斯顿(1993)随机波动模型。蒙特卡洛研究了基于PBEF估计器的有限样本性能，并与Bollerslev和Zhou(2002)基于综合波动率条件矩的GMM估计器的性能进行了比较。本文还研究了对数价格过程受到市场微观结构噪声污染的情况。首先，研究了MMS噪声对两种无噪声校正估计方法的参数估计的影响。其次，利用已实现的核函数代替已实现的方差逼近综合波动率，构造了噪声鲁棒GMM估计器。在噪声环境下对PBEFs进行了重新计算，研究了两种估计方法对噪声的估计能力。蒙特卡罗研究表明，无论在有或没有MMS噪声的情况下，基于PBEFs的估计量都优于GMM估计量。最后，一个实证应用研究了应用基于PBEF的估计器在实践中的可能挑战和一般性能。
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